Overview

Dataset statistics

Number of variables17
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory179.1 B

Variable types

Categorical1
Numeric16

Alerts

Team has a high cardinality: 60 distinct values High cardinality
Tournament is highly correlated with Score and 13 other fieldsHigh correlation
Score is highly correlated with Tournament and 13 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 13 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 13 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 13 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 13 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 13 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 13 other fieldsHigh correlation
TournamentChampion is highly correlated with Tournament and 13 other fieldsHigh correlation
Runnerup is highly correlated with Tournament and 12 other fieldsHigh correlation
TeamLaunch is highly correlated with Tournament and 12 other fieldsHigh correlation
HighestPositionHeld is highly correlated with Tournament and 13 other fieldsHigh correlation
WinningRatio is highly correlated with Tournament and 13 other fieldsHigh correlation
WinTournament is highly correlated with Tournament and 13 other fieldsHigh correlation
LostRatio is highly correlated with Tournament and 12 other fieldsHigh correlation
DrawRatio is highly correlated with TeamLaunchHigh correlation
Tournament is highly correlated with Score and 13 other fieldsHigh correlation
Score is highly correlated with Tournament and 13 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 13 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 13 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 13 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 10 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 13 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 11 other fieldsHigh correlation
TournamentChampion is highly correlated with Tournament and 9 other fieldsHigh correlation
Runnerup is highly correlated with Tournament and 10 other fieldsHigh correlation
TeamLaunch is highly correlated with Tournament and 8 other fieldsHigh correlation
HighestPositionHeld is highly correlated with Tournament and 10 other fieldsHigh correlation
WinningRatio is highly correlated with Tournament and 12 other fieldsHigh correlation
WinTournament is highly correlated with Tournament and 9 other fieldsHigh correlation
LostRatio is highly correlated with Tournament and 12 other fieldsHigh correlation
Tournament is highly correlated with Score and 12 other fieldsHigh correlation
Score is highly correlated with Tournament and 12 other fieldsHigh correlation
PlayedGames is highly correlated with Tournament and 11 other fieldsHigh correlation
WonGames is highly correlated with Tournament and 12 other fieldsHigh correlation
DrawnGames is highly correlated with Tournament and 10 other fieldsHigh correlation
LostGames is highly correlated with Tournament and 9 other fieldsHigh correlation
BasketScored is highly correlated with Tournament and 12 other fieldsHigh correlation
BasketGiven is highly correlated with Tournament and 10 other fieldsHigh correlation
TournamentChampion is highly correlated with Tournament and 9 other fieldsHigh correlation
Runnerup is highly correlated with Tournament and 11 other fieldsHigh correlation
TeamLaunch is highly correlated with HighestPositionHeldHigh correlation
HighestPositionHeld is highly correlated with Tournament and 13 other fieldsHigh correlation
WinningRatio is highly correlated with Tournament and 12 other fieldsHigh correlation
WinTournament is highly correlated with Tournament and 8 other fieldsHigh correlation
LostRatio is highly correlated with Tournament and 12 other fieldsHigh correlation
Team is highly correlated with Tournament and 15 other fieldsHigh correlation
Tournament is highly correlated with Team and 10 other fieldsHigh correlation
Score is highly correlated with Team and 12 other fieldsHigh correlation
PlayedGames is highly correlated with Team and 10 other fieldsHigh correlation
WonGames is highly correlated with Team and 12 other fieldsHigh correlation
DrawnGames is highly correlated with Team and 10 other fieldsHigh correlation
LostGames is highly correlated with Team and 13 other fieldsHigh correlation
BasketScored is highly correlated with Team and 12 other fieldsHigh correlation
BasketGiven is highly correlated with Team and 12 other fieldsHigh correlation
TournamentChampion is highly correlated with Team and 9 other fieldsHigh correlation
Runnerup is highly correlated with Team and 12 other fieldsHigh correlation
TeamLaunch is highly correlated with Team and 1 other fieldsHigh correlation
HighestPositionHeld is highly correlated with Team and 5 other fieldsHigh correlation
WinningRatio is highly correlated with Team and 13 other fieldsHigh correlation
WinTournament is highly correlated with Team and 9 other fieldsHigh correlation
LostRatio is highly correlated with Team and 14 other fieldsHigh correlation
DrawRatio is highly correlated with Team and 2 other fieldsHigh correlation
Team is uniformly distributed Uniform
Team has unique values Unique
Score has unique values Unique
BasketGiven has unique values Unique
TournamentChampion has 51 (85.0%) zeros Zeros
Runnerup has 47 (78.3%) zeros Zeros
WinTournament has 51 (85.0%) zeros Zeros

Reproduction

Analysis started2022-02-23 16:53:56.548469
Analysis finished2022-02-23 16:54:21.220361
Duration24.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Team
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Team 1
 
1
Team 2
 
1
Team 33
 
1
Team 34
 
1
Team 35
 
1
Other values (55)
55 

Length

Max length7
Median length7
Mean length6.85
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st rowTeam 1
2nd rowTeam 2
3rd rowTeam 3
4th rowTeam 4
5th rowTeam 5

Common Values

ValueCountFrequency (%)
Team 11
 
1.7%
Team 21
 
1.7%
Team 331
 
1.7%
Team 341
 
1.7%
Team 351
 
1.7%
Team 361
 
1.7%
Team 371
 
1.7%
Team 381
 
1.7%
Team 391
 
1.7%
Team 401
 
1.7%
Other values (50)50
83.3%

Length

2022-02-23T22:24:21.302048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
team60
50.0%
311
 
0.8%
151
 
0.8%
31
 
0.8%
41
 
0.8%
51
 
0.8%
61
 
0.8%
71
 
0.8%
81
 
0.8%
91
 
0.8%
Other values (51)51
42.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Tournament
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.38333333
Minimum1
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:21.381653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q339
95-th percentile82.2
Maximum86
Range85
Interquartile range (IQR)35

Descriptive statistics

Standard deviation26.88461955
Coefficient of variation (CV)1.1025818
Kurtosis0.1761347883
Mean24.38333333
Median Absolute Deviation (MAD)9.5
Skewness1.197176233
Sum1463
Variance722.7827684
MonotonicityNot monotonic
2022-02-23T22:24:21.476645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
15
 
8.3%
45
 
8.3%
124
 
6.7%
24
 
6.7%
34
 
6.7%
63
 
5.0%
863
 
5.0%
172
 
3.3%
822
 
3.3%
72
 
3.3%
Other values (23)26
43.3%
ValueCountFrequency (%)
15
8.3%
24
6.7%
34
6.7%
45
8.3%
51
 
1.7%
63
5.0%
72
 
3.3%
92
 
3.3%
112
 
3.3%
124
6.7%
ValueCountFrequency (%)
863
5.0%
822
3.3%
801
 
1.7%
731
 
1.7%
701
 
1.7%
581
 
1.7%
512
3.3%
451
 
1.7%
441
 
1.7%
431
 
1.7%

Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean916.45
Minimum14
Maximum4385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:21.548611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile33.4
Q1104.25
median395.5
Q31360.5
95-th percentile3388.8
Maximum4385
Range4371
Interquartile range (IQR)1256.25

Descriptive statistics

Standard deviation1138.342899
Coefficient of variation (CV)1.24212221
Kurtosis1.686503264
Mean916.45
Median Absolute Deviation (MAD)332
Skewness1.574103973
Sum54987
Variance1295824.557
MonotonicityStrictly decreasing
2022-02-23T22:24:21.626762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43851
 
1.7%
42621
 
1.7%
3431
 
1.7%
2931
 
1.7%
2851
 
1.7%
2771
 
1.7%
2421
 
1.7%
2301
 
1.7%
1901
 
1.7%
1881
 
1.7%
Other values (50)50
83.3%
ValueCountFrequency (%)
141
1.7%
191
1.7%
221
1.7%
341
1.7%
351
1.7%
401
1.7%
421
1.7%
521
1.7%
561
1.7%
711
1.7%
ValueCountFrequency (%)
43851
1.7%
42621
1.7%
34421
1.7%
33861
1.7%
33681
1.7%
28191
1.7%
27921
1.7%
25731
1.7%
21091
1.7%
18841
1.7%

PlayedGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.1
Minimum30
Maximum2762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:21.704887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile37.6
Q1115.5
median424.5
Q31345.5
95-th percentile2668.9
Maximum2762
Range2732
Interquartile range (IQR)1230

Descriptive statistics

Standard deviation877.4653926
Coefficient of variation (CV)1.083156885
Kurtosis-0.03039849115
Mean810.1
Median Absolute Deviation (MAD)344.5
Skewness1.123453799
Sum48606
Variance769945.5153
MonotonicityNot monotonic
2022-02-23T22:24:21.782995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27623
 
5.0%
303
 
5.0%
382
 
3.3%
682
 
3.3%
802
 
3.3%
1142
 
3.3%
26641
 
1.7%
1861
 
1.7%
4261
 
1.7%
4481
 
1.7%
Other values (42)42
70.0%
ValueCountFrequency (%)
303
5.0%
382
3.3%
541
 
1.7%
682
3.3%
721
 
1.7%
802
3.3%
901
 
1.7%
1081
 
1.7%
1142
3.3%
1161
 
1.7%
ValueCountFrequency (%)
27623
5.0%
26641
 
1.7%
26261
 
1.7%
26141
 
1.7%
24081
 
1.7%
23021
 
1.7%
19861
 
1.7%
17281
 
1.7%
16981
 
1.7%
15301
 
1.7%

WonGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.0333333
Minimum5
Maximum1647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:21.861121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.95
Q134.75
median124
Q3432.75
95-th percentile1210.6
Maximum1647
Range1642
Interquartile range (IQR)398

Descriptive statistics

Standard deviation408.4813946
Coefficient of variation (CV)1.321803671
Kurtosis2.577188927
Mean309.0333333
Median Absolute Deviation (MAD)104.5
Skewness1.786066522
Sum18542
Variance166857.0497
MonotonicityNot monotonic
2022-02-23T22:24:21.940044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72
 
3.3%
82
 
3.3%
16471
 
1.7%
371
 
1.7%
1291
 
1.7%
1041
 
1.7%
961
 
1.7%
1031
 
1.7%
761
 
1.7%
621
 
1.7%
Other values (48)48
80.0%
ValueCountFrequency (%)
51
1.7%
72
3.3%
82
3.3%
131
1.7%
171
1.7%
181
1.7%
191
1.7%
201
1.7%
211
1.7%
261
1.7%
ValueCountFrequency (%)
16471
1.7%
15811
1.7%
12411
1.7%
12091
1.7%
11871
1.7%
9901
1.7%
9481
1.7%
8641
1.7%
6981
1.7%
6061
1.7%

DrawnGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.0833333
Minimum4
Maximum633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.128836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.9
Q126.25
median98.5
Q3331.5
95-th percentile598.5
Maximum633
Range629
Interquartile range (IQR)305.25

Descriptive statistics

Standard deviation201.9855081
Coefficient of variation (CV)1.051551452
Kurtosis-0.4223342667
Mean192.0833333
Median Absolute Deviation (MAD)82.5
Skewness0.98489917
Sum11525
Variance40798.14548
MonotonicityNot monotonic
2022-02-23T22:24:22.206888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
952
 
3.3%
142
 
3.3%
162
 
3.3%
442
 
3.3%
371
 
1.7%
1021
 
1.7%
1271
 
1.7%
921
 
1.7%
791
 
1.7%
761
 
1.7%
Other values (46)46
76.7%
ValueCountFrequency (%)
41
1.7%
51
1.7%
61
1.7%
81
1.7%
101
1.7%
111
1.7%
131
1.7%
142
3.3%
162
3.3%
181
1.7%
ValueCountFrequency (%)
6331
1.7%
6161
1.7%
6081
1.7%
5981
1.7%
5771
1.7%
5731
1.7%
5521
1.7%
5311
1.7%
5221
1.7%
4401
1.7%

LostGames
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.8166667
Minimum15
Maximum1070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.300986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile19.95
Q162.75
median197.5
Q3563.5
95-th percentile862.3
Maximum1070
Range1055
Interquartile range (IQR)500.75

Descriptive statistics

Standard deviation294.5086394
Coefficient of variation (CV)0.9536682156
Kurtosis-0.4560384548
Mean308.8166667
Median Absolute Deviation (MAD)158.5
Skewness0.8805955594
Sum18529
Variance86735.3387
MonotonicityNot monotonic
2022-02-23T22:24:22.402770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373
 
5.0%
662
 
3.3%
8612
 
3.3%
2952
 
3.3%
521
 
1.7%
1581
 
1.7%
1521
 
1.7%
1181
 
1.7%
1101
 
1.7%
1371
 
1.7%
Other values (45)45
75.0%
ValueCountFrequency (%)
151
 
1.7%
181
 
1.7%
191
 
1.7%
201
 
1.7%
211
 
1.7%
301
 
1.7%
331
 
1.7%
373
5.0%
411
 
1.7%
441
 
1.7%
ValueCountFrequency (%)
10701
1.7%
9201
1.7%
8871
1.7%
8612
3.3%
7751
1.7%
7661
1.7%
7231
1.7%
6821
1.7%
6391
1.7%
6291
1.7%

BasketScored
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.35
Minimum34
Maximum5947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.511494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile37.95
Q1154.5
median444
Q31669.75
95-th percentile4538.85
Maximum5947
Range5913
Interquartile range (IQR)1515.25

Descriptive statistics

Standard deviation1512.063948
Coefficient of variation (CV)1.304234225
Kurtosis2.406122548
Mean1159.35
Median Absolute Deviation (MAD)373.5
Skewness1.75805808
Sum69561
Variance2286337.384
MonotonicityNot monotonic
2022-02-23T22:24:22.588080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702
 
3.3%
59471
 
1.7%
2271
 
1.7%
3931
 
1.7%
2911
 
1.7%
4191
 
1.7%
3201
 
1.7%
2441
 
1.7%
2851
 
1.7%
1991
 
1.7%
Other values (49)49
81.7%
ValueCountFrequency (%)
341
1.7%
361
1.7%
371
1.7%
381
1.7%
511
1.7%
621
1.7%
702
3.3%
711
1.7%
971
1.7%
1011
1.7%
ValueCountFrequency (%)
59471
1.7%
59001
1.7%
46311
1.7%
45341
1.7%
43981
1.7%
36801
1.7%
36091
1.7%
32281
1.7%
26831
1.7%
22781
1.7%

BasketGiven
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.233333
Minimum55
Maximum3889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.666200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile65.95
Q1236
median632.5
Q32001.25
95-th percentile3377.8
Maximum3889
Range3834
Interquartile range (IQR)1765.25

Descriptive statistics

Standard deviation1163.946914
Coefficient of variation (CV)1.004066119
Kurtosis-0.4510570834
Mean1159.233333
Median Absolute Deviation (MAD)497.5
Skewness0.9581639422
Sum69554
Variance1354772.419
MonotonicityNot monotonic
2022-02-23T22:24:22.759938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31401
 
1.7%
31141
 
1.7%
6621
 
1.7%
4891
 
1.7%
5881
 
1.7%
4101
 
1.7%
3661
 
1.7%
4301
 
1.7%
2411
 
1.7%
2961
 
1.7%
Other values (50)50
83.3%
ValueCountFrequency (%)
551
1.7%
571
1.7%
651
1.7%
661
1.7%
851
1.7%
1151
1.7%
1161
1.7%
1171
1.7%
1311
1.7%
1391
1.7%
ValueCountFrequency (%)
38891
1.7%
37001
1.7%
34691
1.7%
33731
1.7%
33091
1.7%
32301
1.7%
31401
1.7%
31141
1.7%
28471
1.7%
26241
1.7%

TournamentChampion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.45
Minimum0
Maximum33
Zeros51
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.822427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.1
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.515540449
Coefficient of variation (CV)3.803820999
Kurtosis23.32619157
Mean1.45
Median Absolute Deviation (MAD)0
Skewness4.73484465
Sum87
Variance30.42118644
MonotonicityNot monotonic
2022-02-23T22:24:22.884912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
051
85.0%
13
 
5.0%
331
 
1.7%
251
 
1.7%
101
 
1.7%
61
 
1.7%
81
 
1.7%
21
 
1.7%
ValueCountFrequency (%)
051
85.0%
13
 
5.0%
21
 
1.7%
61
 
1.7%
81
 
1.7%
101
 
1.7%
251
 
1.7%
331
 
1.7%
ValueCountFrequency (%)
331
 
1.7%
251
 
1.7%
101
 
1.7%
81
 
1.7%
61
 
1.7%
21
 
1.7%
13
 
5.0%
051
85.0%

Runnerup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.433333333
Minimum0
Maximum25
Zeros47
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:22.932383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.05
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.574678526
Coefficient of variation (CV)3.191636181
Kurtosis19.48211736
Mean1.433333333
Median Absolute Deviation (MAD)0
Skewness4.321793839
Sum86
Variance20.92768362
MonotonicityNot monotonic
2022-02-23T22:24:23.004382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
047
78.3%
15
 
8.3%
231
 
1.7%
251
 
1.7%
81
 
1.7%
61
 
1.7%
71
 
1.7%
41
 
1.7%
31
 
1.7%
51
 
1.7%
ValueCountFrequency (%)
047
78.3%
15
 
8.3%
31
 
1.7%
41
 
1.7%
51
 
1.7%
61
 
1.7%
71
 
1.7%
81
 
1.7%
231
 
1.7%
251
 
1.7%
ValueCountFrequency (%)
251
 
1.7%
231
 
1.7%
81
 
1.7%
71
 
1.7%
61
 
1.7%
51
 
1.7%
41
 
1.7%
31
 
1.7%
15
 
8.3%
047
78.3%

TeamLaunch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1957.95
Minimum1929
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.101480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1929
5-th percentile1929
Q11934.75
median1950.5
Q31977.25
95-th percentile2007.1
Maximum2016
Range87
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation26.6467316
Coefficient of variation (CV)0.01360950566
Kurtosis-0.7874722188
Mean1957.95
Median Absolute Deviation (MAD)20
Skewness0.6825695415
Sum117477
Variance710.0483051
MonotonicityNot monotonic
2022-02-23T22:24:23.172638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
192910
 
16.7%
19413
 
5.0%
19632
 
3.3%
19772
 
3.3%
19352
 
3.3%
19512
 
3.3%
19392
 
3.3%
20161
 
1.7%
19471
 
1.7%
19941
 
1.7%
Other values (34)34
56.7%
ValueCountFrequency (%)
192910
16.7%
19301
 
1.7%
19311
 
1.7%
19321
 
1.7%
19331
 
1.7%
19341
 
1.7%
19352
 
3.3%
19392
 
3.3%
19401
 
1.7%
19413
 
5.0%
ValueCountFrequency (%)
20161
1.7%
20141
1.7%
20091
1.7%
20071
1.7%
20041
1.7%
19991
1.7%
19981
1.7%
19961
1.7%
19951
1.7%
19941
1.7%

HighestPositionHeld
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.05
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.259545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum20
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.315232348
Coefficient of variation (CV)0.7539336664
Kurtosis-0.2849335823
Mean7.05
Median Absolute Deviation (MAD)4
Skewness0.8321643556
Sum423
Variance28.25169492
MonotonicityNot monotonic
2022-02-23T22:24:23.333182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
19
15.0%
46
10.0%
25
8.3%
65
8.3%
75
8.3%
84
 
6.7%
54
 
6.7%
34
 
6.7%
104
 
6.7%
173
 
5.0%
Other values (8)11
18.3%
ValueCountFrequency (%)
19
15.0%
25
8.3%
34
6.7%
46
10.0%
54
6.7%
65
8.3%
75
8.3%
84
6.7%
91
 
1.7%
104
6.7%
ValueCountFrequency (%)
201
 
1.7%
191
 
1.7%
173
5.0%
163
5.0%
151
 
1.7%
141
 
1.7%
122
3.3%
111
 
1.7%
104
6.7%
91
 
1.7%

WinningRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3136478999
Minimum0.1666666667
Maximum0.5963070239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.425279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1666666667
5-th percentile0.2105263158
Q10.2760749417
median0.3049172226
Q30.3354016446
95-th percentile0.447029609
Maximum0.5963070239
Range0.4296403572
Interquartile range (IQR)0.05932670286

Descriptive statistics

Standard deviation0.07831199491
Coefficient of variation (CV)0.2496812347
Kurtosis3.587114097
Mean0.3136478999
Median Absolute Deviation (MAD)0.03103205187
Skewness1.440045715
Sum18.81887399
Variance0.006132768547
MonotonicityNot monotonic
2022-02-23T22:24:23.508223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23333333332
 
3.3%
0.21052631582
 
3.3%
0.29078014182
 
3.3%
0.252
 
3.3%
0.59630702391
 
1.7%
0.29444444441
 
1.7%
0.30281690141
 
1.7%
0.23214285711
 
1.7%
0.27745664741
 
1.7%
0.30838323351
 
1.7%
Other values (46)46
76.7%
ValueCountFrequency (%)
0.16666666671
1.7%
0.19117647061
1.7%
0.21052631582
3.3%
0.22807017541
1.7%
0.23214285711
1.7%
0.23333333332
3.3%
0.23751
1.7%
0.24342105261
1.7%
0.2474402731
1.7%
0.252
3.3%
ValueCountFrequency (%)
0.59630702391
1.7%
0.57241129621
1.7%
0.47475133891
1.7%
0.44557057061
1.7%
0.43772628531
1.7%
0.41176470591
1.7%
0.41112956811
1.7%
0.37532580361
1.7%
0.36797385621
1.7%
0.36100533131
1.7%

WinTournament
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01749521194
Minimum0
Maximum0.3837209302
Zeros51
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.572905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.09462209302
Maximum0.3837209302
Range0.3837209302
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0644266104
Coefficient of variation (CV)3.682528147
Kurtosis22.69638073
Mean0.01749521194
Median Absolute Deviation (MAD)0
Skewness4.660696709
Sum1.049712716
Variance0.004150788128
MonotonicityNot monotonic
2022-02-23T22:24:23.746411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
051
85.0%
0.38372093021
 
1.7%
0.29069767441
 
1.7%
0.1251
 
1.7%
0.073170731711
 
1.7%
0.093023255811
 
1.7%
0.013698630141
 
1.7%
0.028571428571
 
1.7%
0.019607843141
 
1.7%
0.022222222221
 
1.7%
ValueCountFrequency (%)
051
85.0%
0.013698630141
 
1.7%
0.019607843141
 
1.7%
0.022222222221
 
1.7%
0.028571428571
 
1.7%
0.073170731711
 
1.7%
0.093023255811
 
1.7%
0.1251
 
1.7%
0.29069767441
 
1.7%
0.38372093021
 
1.7%
ValueCountFrequency (%)
0.38372093021
 
1.7%
0.29069767441
 
1.7%
0.1251
 
1.7%
0.093023255811
 
1.7%
0.073170731711
 
1.7%
0.028571428571
 
1.7%
0.022222222221
 
1.7%
0.019607843141
 
1.7%
0.013698630141
 
1.7%
051
85.0%

LostRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4499194853
Minimum0.2038377987
Maximum0.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.809543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2038377987
5-th percentile0.3207063774
Q10.4114297142
median0.4572710678
Q30.4854244889
95-th percentile0.5753703704
Maximum0.7
Range0.4961622013
Interquartile range (IQR)0.07399477473

Descriptive statistics

Standard deviation0.08400880838
Coefficient of variation (CV)0.186719649
Kurtosis1.958554246
Mean0.4499194853
Median Absolute Deviation (MAD)0.03888130969
Skewness-0.2685612917
Sum26.99516912
Variance0.007057479886
MonotonicityNot monotonic
2022-02-23T22:24:23.903558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52
 
3.3%
0.46252
 
3.3%
0.20383779871
 
1.7%
0.51315789471
 
1.7%
0.47417840381
 
1.7%
0.4843751
 
1.7%
0.45664739881
 
1.7%
0.45508982041
 
1.7%
0.4370370371
 
1.7%
0.48245614041
 
1.7%
Other values (48)48
80.0%
ValueCountFrequency (%)
0.20383779871
1.7%
0.22013034031
1.7%
0.29648048971
1.7%
0.32198142411
1.7%
0.32319819821
1.7%
0.33309196231
1.7%
0.36835548171
1.7%
0.37402258911
1.7%
0.37581699351
1.7%
0.38569989931
1.7%
ValueCountFrequency (%)
0.71
1.7%
0.60294117651
1.7%
0.61
1.7%
0.57407407411
1.7%
0.56896551721
1.7%
0.55555555561
1.7%
0.53333333331
1.7%
0.52631578951
1.7%
0.51388888891
1.7%
0.51315789471
1.7%

DrawRatio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.236176991
Minimum0.1111111111
Maximum0.3859649123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2022-02-23T22:24:23.984173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1111111111
5-th percentile0.1441187739
Q10.2233043652
median0.2437241782
Q30.2621629029
95-th percentile0.2875986842
Maximum0.3859649123
Range0.2748538012
Interquartile range (IQR)0.03885853774

Descriptive statistics

Standard deviation0.04458671442
Coefficient of variation (CV)0.1887851743
Kurtosis2.523733595
Mean0.236176991
Median Absolute Deviation (MAD)0.01916986497
Skewness-0.3844442746
Sum14.17061946
Variance0.001987975103
MonotonicityNot monotonic
2022-02-23T22:24:24.086148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.24561403512
 
3.3%
0.25910931172
 
3.3%
0.19985517741
 
1.7%
0.22300469481
 
1.7%
0.26589595381
 
1.7%
0.23652694611
 
1.7%
0.28148148151
 
1.7%
0.22340425531
 
1.7%
0.281251
 
1.7%
0.2473118281
 
1.7%
Other values (48)48
80.0%
ValueCountFrequency (%)
0.11111111111
1.7%
0.13333333331
1.7%
0.13793103451
1.7%
0.14444444441
1.7%
0.14814814811
1.7%
0.16153846151
1.7%
0.16666666671
1.7%
0.19444444441
1.7%
0.19985517741
1.7%
0.20588235291
1.7%
ValueCountFrequency (%)
0.38596491231
1.7%
0.31
1.7%
0.28947368421
1.7%
0.28751
1.7%
0.28348214291
1.7%
0.28148148151
1.7%
0.281251
1.7%
0.26666666671
1.7%
0.266253871
1.7%
0.26589595381
1.7%

Interactions

2022-02-23T22:24:19.428417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:23:58.581067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:00.297685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:01.944118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:03.227214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:04.623128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:06.070790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:07.242925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:08.556780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:09.843818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:11.291789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:12.526987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:13.909710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:15.345164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:16.577148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:18.289773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:19.500989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:23:58.663415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:00.396029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:02.037071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:03.291928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:04.704518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:06.151053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:07.315507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:08.636847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:09.934034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:11.364820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:12.596704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:13.976836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:15.410757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:16.669410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:18.359699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:19.563943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:23:58.737663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:00.504176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:02.106169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:03.371167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:04.784960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:06.229535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:07.395254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:08.710070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:10.018598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:11.448668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:12.667797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:14.055080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:15.474206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:16.773927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:18.422962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:19.626694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:23:58.827210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:00.586258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:02.170985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:03.434325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-23T22:24:04.857339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-02-23T22:24:19.365764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-23T22:24:24.198295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-23T22:24:24.366916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-23T22:24:24.520012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-23T22:24:24.673445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-23T22:24:20.855315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-23T22:24:21.142103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TeamTournamentScorePlayedGamesWonGamesDrawnGamesLostGamesBasketScoredBasketGivenTournamentChampionRunnerupTeamLaunchHighestPositionHeldWinningRatioWinTournamentLostRatioDrawRatio
0Team 186438527621647552563594731403323192910.5963070.3837210.2038380.199855
1Team 286426227621581573608590031142525192910.5724110.2906980.2201300.207458
2Team 38034422614124159877545343309108192910.4747510.1250000.2964800.228768
3Team 4823386266411876168614398346966193110.4455710.0731710.3231980.231231
4Team 5863368276212096339204631370087192910.4377260.0930230.3330920.229182
5Team 673281924089905318873680337314193410.4111300.0136990.3683550.220515
6Team 7822792262694860810703609388900192930.3610050.0000000.4074640.231531
7Team 870257323028645778613228323023192910.3753260.0285710.3740230.250652
8Team 958210919866985227662683284701193920.3514600.0000000.3857000.262840
9Team 1051188417286064406822159249210193210.3506940.0196080.3946760.254630

Last rows

TeamTournamentScorePlayedGamesWonGamesDrawnGamesLostGamesBasketScoredBasketGivenTournamentChampionRunnerupTeamLaunchHighestPositionHeldWinningRatioWinTournamentLostRatioDrawRatio
50Team 5137190291348121183001953140.3222220.00.5333330.144444
51Team 524567221143715318400192960.2916670.00.5138890.194444
52Team 532526817183371116001979100.2500000.00.4852940.264706
53Team 5434254186309713100192980.3333330.00.5555560.111111
54Team 552406813144170182001950160.1911760.00.6029410.205882
55Team 5613538811193655002016170.2105260.00.5000000.289474
56Team 5713438810203866002009200.2105260.00.5263160.263158
57Team 581223078153757001956160.2333330.00.5000000.266667
58Team 591193075185185001951160.2333330.00.6000000.166667
59Team 601143054213465001955150.1666670.00.7000000.133333